Background estimation and subtraction is a common method used for change detection (also known as motion detection) that is a first stage in many object tracking, object counting, and/or background substitution algorithms. The key challenges in this problem are, sensitivity to noise, sensitivity to slow intensity variations, the rate at which the background learns an object that was moving but has now come to a stop, and the rate at which the background unlearns an object that was stationary earlier, but has started to move now.
The background should be able to adapt to abandoned objects and initial objects that move out. The abandoned objects must be blended into the background, while objects that were initially present in the background but later moved must be erased from the background. Existing methods use a single estimated background image, which tends to have weaknesses in learning objects that have come to a stop or unlearning objects that have started to move. Further, existing methods used for change detection may perform motion detection on every input pixel, which results in a higher computational complexity and are not robust enough to include the entire moving object.
Other features of the present embodiments will be apparent from the accompanying drawings and from the detailed description that follows.